Estimate of Swimming Energy Expenditure Utilizing an Omnidirectional Accelerometer and Swim Performance Measures Jeanne D. Johnston and Joel M. Stager, FACSM Counsilman Center for the Science of Swimming Human Performance Laboratory, Department of Kinesiology, Indiana University, Bloomington, IN INTRODUCTION PURPOSE Examine the relationship between accelerometer output and the determinants of swim velocity as a means to evaluate the efficacy of an omnidirectional accelerometer (ODA) to quantify swimming energy expenditure (SWE).  The relationship between accelerometer counts and energy expenditure while swimming may be non-linear due to the relationship among swim efficiency, swim velocity, and the resistive forces. M ETHODS  Age, height, and weight were recorded for each subject.  Eight men (26.5 years, +/-8.8) and 10 women (27.1 years, +/-9.8) performed three submaximal, constant paced, meter (400 yard) front crawl swims.  Subjects were instructed to maintain a constant paced swim at ‘light, moderate and hard’ intensities of their choosing. Swimming velocities ranged from m · sec- 1.  Each subject completed one swim at a common velocity of 1 m·sec -1. Subjects were pace by a visual and audio cue.  An ODA (Mini-Mitter Corporation, Actical accelerometer) was worn on the right wrist, waist, and leg. Accelerometer counts and heart rate were recorded for each 15-second period and averaged over one minute.  Expired gases were collected for 20 seconds at the completion of each swim to determine oxygen consumption (Costill, et al., 1985).  Multiple regression techniques were utilized to develop equations to estimate SWE (kcal  kg -1  min -1 ) based on accelerometer counts, swim performance traits, and subject characteristics.  Significance was set at p = 0.05 D ISCUSSION C ONCLUSION Valid estimates of SWE can be obtained through a combination of variables including ODA accelerometer counts, swim velocity, and subject characteristics. The amount of variance accounted for with these variables (R 2 values) is similar to algorithms generated for land-based activities. Supported, in part, by United States Master Swimming. A CKNOWLEDGEMENT R ESULTS ABSTRACT Energy expenditure, as it relates to velocity of movement, differs in swimming when compared to land based activities. Investigations examining land based activities have demonstrated a linear relationship between accelerometer counts and energy expenditure thus allowing their use in determining energy balance. Due to the complex relationship among swim efficiency, swim velocity and drag, it is likely that a non- linear relationship will exist among them while swimming. Purpose: Examine the relationship between accelerometer output and the determinants of swim velocity as a means to evaluate the efficacy of an Omnidirectional accelerometer (ODA) to quantify swimming energy expenditure (SWE). Methods: Eight men (26.5 years, +/-8.8) and 10 women (27.1 years, +/-9.8) performed three submaximal meter (400 yard) front crawl swims. Expired gases were collected for 20 seconds at the completion of each swim to determine oxygen consumption. An ODA was worn on the right wrist, waist, and right leg. Multiple regression techniques were utilized to develop prediction equations for SWE (kcal  kg-1  min-1) based on accelerometer counts, swim performance traits, and subject characteristics. Results: Significant correlations between measured VO 2 and ODA output were noted for men (leg, r = 0.82, p< 0.01; waist, r = 0.48, p< 0.05) and women (arm, r = 0.74, p< 0.01; leg, r = 0.42, p < 0.05). Age and height were included in the prediction equation for both men and women. The best linear prediction equations included leg counts for the men (R 2 = 0.72, SEE =.0333 kcal  kg -1  min -1 ) and wrist counts for the women (R 2 = 0.62, SEE =.0432 kcal  kg -1  min -1 ). The equation for the males was significantly improved (p<0.05) with the addition of second monitor (waist) expressed as a non- linear component (waist + waist 2 ; R 2 =.80, SEE =.0300 kcal  kg -1  min -1 ). The equation for the females was significantly improved by replacing the wrist value with a squared wrist value and adding the swim velocity (R 2 =.65, SEE =.0429 kcal  kg -1  min -1 ). The best overall equation for the women was obtained when leg counts and heart rate were added to this equation (R 2 =.72, SEE =.0392 kcal  kg -1  min -1 ). Conclusion: Valid estimates of SWE can be obtained through a combination of variables including ODA accelerometer counts, swim velocity, and subject characteristics. The amount of variance accounted for with these variables (R 2 values) is similar to algorithms generated for land-based activities.  Energy expenditure, as it relates to velocity of movement, differs in swimming when compared to land based activities.  Investigations examining land based activities have demonstrated a linear relationship between accelerometer counts and energy expenditure thus allowing their use in determining energy balance.  Due to the complex relationship among swim efficiency, swim velocity and drag, it is likely that a non-linear relationship will exist among them while swimming. H YPOTHESES Sex Age (years)Height (cm)Weight (kg) Male26.5 +/ / /-9.7 Female27.1 +/ / /-10.1 Table 1. Subject characteristics Sex ArmLegWaist Male **.481* Female.741**.421*.231 Correlation Significant at.01**,.05* (2-tailed) Table 2. Correlation coefficients between measured SWE and ODA counts  Significant differences were observed for height (p < 0.05), weight (p < 0.01), swimming velocity (p < 0.05) and SWE (p < 0.05) when comparing sexes. ModelPredictorsR2R2 SEE Model 1 MalesLeg Model 2 MalesLeg + Waist + Waist Model 1 FemalesArm Model 2 FemalesArm 2 + Velocity Model 3 FemalesArm 2 + Velocity+ Leg + HR Table 3. Model Comparisons for Males and Females  The relationship between accelerometer output and energy expenditure varies with the sex of the subject and accelerometer placement.  The relationship between accelerometer output and swim velocity appears to be non-linear within the male population.  Differences in accelerometer counts at a given velocity may reflect differences in skill and/or economy across a restricted range of velocities.  The relationship between accelerometer output and energy expenditure appears to be linear in nature; however, consideration of non-linear components significantly improved the estimation of energy expended while swimming.  The inclusion of multiple monitors significantly improved the estimates of SWE.  The addition of swim performance characteristics (swim velocity) significantly improved the estimates of SWE. Figures 1-2. The relationship between energy expenditure and swim velocity was not the same for both sexes. Figures 3-4. There was a weak (R 2 = 0.26) relationship between arm counts and swim velocity and a strong linear relationship with energy expenditure (R 2 = 0.55) Figures 4-5. There was a strong non-linear relationship (R 2 = 0.61) between leg counts and swim velocity, and a linear relationship between energy expenditure (R 2 = 0.67).  Age and height were included in the prediction equation for both men and women  There is no indication of bias towards under or overestimation across the range of measured values for either the males or females.  Measured and predicted values for energy expenditure via the models were not significantly different ( p < 0.05). Figures 6-7. There was a strong (R 2 = 0.67) non-linear relationship between waist counts and swim velocity for males.  Significant correlations between measured VO 2 and ODA output were noted for men (leg, r = 0.82, p< 0.01; waist, r = 0.48, p< 0.05) and women (arm, r = 0.74, p< 0.01; leg, r = 0.42, p < 0.05).